閾值與平滑處理

灰度圖

import cv2 #opencv讀取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
%matplotlib inline 

img=cv2.imread('cat.jpg')
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape

 

圖像閾值

ret, dst = cv2.threshold(src, thresh, maxval, type)

• src： 輸入圖，只能輸入單通道圖像，通常來說為灰度圖
• dst： 輸出圖
• thresh： 閾值
• maxval： 當像素值超過了閾值（或者小於閾值，根據type來決定），所賦予的值

• type：二值化操作的類型，包含以下5種類型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV

• cv2.THRESH_BINARY 超過閾值部分取maxval（最大值），否則取0

• cv2.THRESH_BINARY_INV THRESH_BINARY的反轉
• cv2.THRESH_TRUNC 大於閾值部分設為閾值，否則不變
• cv2.THRESH_TOZERO 大於閾值部分不改變，否則設為0
• cv2.THRESH_TOZERO_INV THRESH_TOZERO的反轉

ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in range(6):
    plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()

 

圖像平滑

img = cv2.imread('lenaNoise.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 均值濾波
# 簡單的平均卷積操作
blur = cv2.blur(img, (3, 3))

cv2.imshow('blur', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 方框濾波
# 基本和均值一樣，可以選擇歸一化，False越界會產生高亮圖
box = cv2.boxFilter(img,-1,(3,3), normalize=True)  

cv2.imshow('box', box)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 高斯濾波
# 高斯模糊的卷積核里的數值是滿足高斯分布，相當於更重視中間的
aussian = cv2.GaussianBlur(img, (5, 5), 1)  

cv2.imshow('aussian', aussian)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 中值濾波
# 相當於用中值代替
median = cv2.medianBlur(img, 5)  # 中值濾波

cv2.imshow('median', median)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 展示所有的
res = np.hstack((blur,aussian,median))
#print (res)
cv2.imshow('median vs average', res)
cv2.waitKey(0)
cv2.destroyAllWindows()